Article
From Data to Justice: How Machine Learning Advances Legal Analytics
- 1 Taipei Medical University, Taiwan
- 2 Shoolini University, Solan, India
- 3 Ministry of Labour and Employment, Government of India
Abstract
Machine Learning (ML) is revolutionizing legal analytics by facilitating a more evidence-based, efficient, and transparent approach to legal decision-making. This paper examines the application of machine learning (ML) in legal practice through fundamental methodologies, including supervised and unsupervised learning, natural language processing (NLP), and the interpretability of trained models, as well as practical applications and use cases in areas such as case prediction, contract review, compliance monitoring, and sentiment analysis. The paper also explores important ethical, legal, and operational challenges associated with ML in the legal space, namely bias, privacy, occlusion, and liability. By calling for an interdisciplinary lens, the paper highlights emerging approaches, such as fairness-aware and privacy-preserving ML, while noting the idea of having both governance frameworks in place and a common language for responsibly deploying ML. In the end, the analysis emphasizes ethically-informed, transparent ML systems that advance legal integrity and ultimately access to justice.
Keywords
How to cite (APA 7)
Solomon, D. D., Sood, R. P., Gupta, M., & Singh, R. (2025). From Data to Justice: How Machine Learning Advances Legal Analytics. International Journal of Justice and Police Sciences, 1(1), 150–167. https://doi.org/10.5281/zenodo.15717692